Ale CabreraIP Intelligence · Early 2026
MCP Server for IP Intelligence Data
A direct way for AI agents to use IP intelligence data, taken from a signal to a launched integration in 1 month, without breaking a single existing customer.
Outcome
- 1mofrom signal to launched
- 0breaking changes for existing customers
- ↑adoption grew organically, with developers using it several times a day
- IP intelligence had always been delivered through the API.
- Found an MCP server that someone had already built on top of the API. Developers were not waiting for an official one.
- No internal playbook existed for this kind of integration.
- The API already existed. The point of MCP was to get the data into the developer's workflow, inside the AI tools they already used.
- Prototyped and talked to developers before committing.
- Set API key planning and versioning strategy with engineering, so nothing broke for existing integrations at scale.
- Left the official Claude and ChatGPT connectors out of v1 on purpose, to launch early and learn from real usage.
- Partnered with engineering to build the MCP server, letting AI agents query IP intelligence data directly.
- Distributed it as open source, in the docs and to existing customers.
- No formal beta list. Users picked it up and it grew organically.
- Applied rate limiting to early users to de-risk the rollout.
My Role
Spotted the opportunity. Owned the integration strategy, the API versioning approach and the rate-limit design for the rollout.
Engineering Collaboration
Partnered with engineering on the build. Set API key planning and versioning together, so new attributes could be added without breaking changes for customers at scale.
Problem & Risk
The value risk: nobody knew yet if developers would use IP data through agents. The business risk: the existing customer base. Any change to keys, attributes or versions could break integrations at scale.
Customer Discovery & Validation
- An MCP server already built by someone outside the company, on top of the API.
- A prototype and conversations with developers before committing.
- After launch, developers picked it up without a formal beta and used it several times a day.
Context
An API product with a developer audience, at the moment AI agents and MCP were becoming part of how developers work.
Timeline
What We Did
- Launched an MCP server for the API.
- Set API key planning and versioning strategy with engineering.
- Applied rate limiting to early users, to prevent breaking changes as usage scaled.
- Distributed through open source, docs and existing customers.
- Opened the full IP intelligence data to agents: an agent can ask for any information the data holds.
MCP server
an AI agent asks, the API answers
IP data returned to the agent
existing integrations unchanged
early users rate limited
Who Benefits
Developers building with AI agents get the data inside the tools they already use, without writing their own integration. Existing customers were not affected.
Next Steps
The official connectors for Claude and ChatGPT, left out of v1 on purpose.